1089 Evaluation of Age-Appropriate Sleep Metrics in Pediatric Patients Undergoing Overnight Polysomnography at Geisinger
Bibliographic record
Abstract
Abstract Introduction The American Academy of Sleep Medicine (AASM) recommends age-specific total sleep time (TST) and REM sleep percentages for optimal pediatric health. This study evaluates whether pediatric patients undergoing overnight polysomnography (PSG) at our Geisinger Sleep Lab achieve these recommended sleep metrics. Identifying deviations from guideline-based sleep parameters can inform targeted interventions to improve pediatric sleep outcomes. Methods The Geisinger Sleep Medicine database was queried for children ages 1-17 at the Geisinger South Wilkes-Barre lab between December 2023 and November 2024. TST, REM sleep percentage, and sleep onset time were extracted. Data were compared to the AASM consensus guidelines for recommended sleep durations and REM percentages for pediatric patients. Results The average TST ranged from 397 minutes (6.6 hours) at age 1 to 436 minutes (7.3 hours) at age 4, falling below AASM recommendations of 11-14 hours for children aged 1-2 years and 10-13 hours for ages 3-5 years. For ages 5-17, average TST varied from 431.7 minutes (7.2 hours) at age 5 to 412.7 minutes (6.9 hours) at age 9, consistently below the recommended 9-12 hours for ages 6-12 and 8-10 hours for teenagers. Average REM percentages ranged from 4.9% to 16.6%, lower than the expected 20-25%. Younger children achieved approximately 30-40 minutes more TST than older children. Sleep onset times ranged from 9:23 PM to 10:50 PM, later than recommended for these age groups. Conclusion Our findings suggest that most pediatric patients in our sleep lab do not achieve age-appropriate sleep metrics as defined by AASM guidelines, potentially underdiagnosing sleep-disordered breathing severity. Reduced TST and REM percentages likely reflect delayed sleep onset and circadian misalignment, exacerbated by inconsistent PSG start times. Contributing factors include late bedtimes, increased screen exposure, and inconsistent parental enforcement of sleep hygiene among older children. Stricter parental regulation of younger children’s routines and stronger homeostatic drive may explain their relatively longer TST. Addressing environmental and scheduling factors could optimize sleep in this population and improve testing quality. Future studies should evaluate whether educating patients and parents and increasing sleep opportunities in the laboratory can enhance the characterization of sleep-disordered breathing. Support (if any)
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".